Automatic Political Profiling in Heterogeneous Corpora

In this paper we consider automatic political tendency recognition in a variety of genres. To this end, four different types of texts in Hebrew with varying levels of political content (manifestly political, semipolitical, non-political) are examined. It is found that in each case, training and testing in the same genre yields strong results. More significantly, training on political texts yields classifiers sufficiently strong to classify non-political personal Facebook pages with fair accuracy. This suggests that individuals’ political tendencies can be identified without recourse to any tagged personal data.

[1]  Rob Malouf,et al.  A Preliminary Investigation into Sentiment Analysis of Informal Political Discourse , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[2]  David Yarowsky,et al.  Classifying latent user attributes in twitter , 2010, SMUC '10.

[3]  Vasileios Hatzivassiloglou,et al.  Automatic Detection of Tags for Political Blogs , 2010, HLT-NAACL 2010.

[4]  M. Laver,et al.  Extracting Policy Positions from Political Texts Using Words as Data , 2003, American Political Science Review.

[5]  Moshe Koppel,et al.  Determining an author's native language by mining a text for errors , 2005, KDD '05.

[6]  Shlomo Argamon,et al.  Effects of Age and Gender on Blogging , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[7]  David Madigan,et al.  Large-Scale Bayesian Logistic Regression for Text Categorization , 2007, Technometrics.

[8]  Katja Filippova,et al.  User Demographics and Language in an Implicit Social Network , 2012, EMNLP.

[9]  Jahna Otterbacher,et al.  Inferring gender of movie reviewers: exploiting writing style, content and metadata , 2010, CIKM.

[10]  Adrian Popescu,et al.  Mining User Home Location and Gender from Flickr Tags , 2010, ICWSM.

[11]  T. Graepel,et al.  Private traits and attributes are predictable from digital records of human behavior , 2013, Proceedings of the National Academy of Sciences.

[12]  Gregory Grefenstette,et al.  Coupling Niche Browsers and Affect Analysis for an Opinion Mining Application , 2004, RIAO.

[13]  Anita Sharma,et al.  Personality and Patterns of Facebook Usage , 2016 .

[14]  Samuel D. Gosling,et al.  Manifestations of Personality in Online Social Networks: Self-Reported Facebook-Related Behaviors and Observable Profile Information , 2011, Cyberpsychology Behav. Soc. Netw..

[15]  Sara Rosenthal,et al.  Age Prediction in Blogs: A Study of Style, Content, and Online Behavior in Pre- and Post-Social Media Generations , 2011, ACL.